Benchmark Dataset for Timetable Optimization of Bus Routes in the City of New Delhi

TL;DR

This paper introduces a new GPS dataset for over 500 bus routes in New Delhi and proposes a timetable optimization algorithm that reduces waiting times, serving as a benchmark for future research.

Contribution

It provides a novel real-time GPS dataset for Delhi buses and an innovative timetable optimization approach using constrained clustering, establishing a benchmark for the dataset.

Findings

Reduced bus waiting times in Delhi using the proposed timetable.

The dataset enables modeling of traffic and transit optimization tasks.

The clustering algorithm effectively classifies bus trips for timetable planning.

Abstract

Public transport is one of the major forms of transportation in the world. This makes it vital to ensure that public transport is efficient. This research presents a novel real-time GPS bus transit data for over 500 routes of buses operating in New Delhi. The data can be used for modeling various timetable optimization tasks as well as in other domains such as traffic management, travel time estimation, etc. The paper also presents an approach to reduce the waiting time of Delhi buses by analyzing the traffic behavior and proposing a timetable. This algorithm serves as a benchmark for the dataset. The algorithm uses a constrained clustering algorithm for classification of trips. It further analyses the data statistically to provide a timetable which is efficient in learning the inter- and intra-month variations.